Multimodal Event Detection in Twitter Hashtag Networks
Yasin Yilmaz, Alfred Hero

TL;DR
This paper introduces a multimodal event detection method for Twitter that combines text and geolocation data using a generative model and EM algorithm, effectively identifying events and hashtags in large datasets.
Contribution
It proposes a novel generative latent variable model and an efficient EM-based learning algorithm for multimodal event detection in Twitter data.
Findings
Effective detection of events and hashtags in large Twitter datasets
The method is computationally efficient and scalable
Experimental results demonstrate the approach's efficacy
Abstract
Event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lends itself to big datasets. Experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Text Analysis Techniques · Web Data Mining and Analysis
